DraftPilot vs ThoughtRiver: how they compare in 2026
DraftPilot and ThoughtRiver both apply company positions to incoming contracts and extend analysis beyond a single agreement. DraftPilot emphasizes an editable standard created from prior legal work, with Word tracked changes and an explicit division between playbook owners and approved reviewers. ThoughtRiver provides a concept based review system through Lexible, with prepared playbooks that vary in breadth and risk tolerance. Its Core, Standard, and Comprehensive options give a team different starting depths, while custom playbooks encode its own positions. Contracts can enter ThoughtRiver through email, upload, or a Word add in, and issues can be shared with colleagues. DraftPilot is relevant where the central problem is governing who applies and changes the legal standard. ThoughtRiver offers a different starting point where the team wants a prepared review framework, supported customization, and several intake routes.
At a glance
All 15 axes, side by side
The same grid applied to every vendor in the index, graded from public sources. Hover a grade to see what the letter means on that axis.
AI Centrality
How much of the product is actually AI. Whether the machine learning is the mechanism the buyer is paying for or a feature layered onto conventional software, and whether the vendor is specific about which is which.
DraftPilot's main product is model driven review and drafting inside Word. A selected playbook supplies preferred positions and fallbacks; the AI identifies departures, proposes language, and explains changes to the reviewer. The same models generate playbooks from past work and support conversational drafting, comparison, and research. Mass Review uses AI to answer questions across agreements and connect findings with source language. Conventional functions such as workspaces, permissions, and file conversion support that workflow, but they are not an independent contract management system. Removing the models would remove the review, analysis, and drafting service that customers buy.
Machine learning is the mechanism the customer pays for. A model reads the contract, answers the Lexible question set, scores the answers against a playbook and produces the risk rating, issues list and suggested redlines. Remove the models and there is no review, no issues list and no redlines, only a document store. The vendor has sold this as an AI product since 2016 and describes the current engine as a proprietary generative model on several large language models.
Citation Accuracy and Hallucination Disclosure
Whether the vendor publishes measured accuracy on citations and assertions, grounds output to primary sources, and says plainly what its system does when it does not know. Legal has a documented public record of fabricated citations reaching filed briefs, so an untested claim of accuracy is not evidence.
AI Chat uses a curated legal network containing legislation, secondary sources, and commentary, with general web access excluded from legal research. Its research guide instructs users to specify the jurisdiction and inspect the list of source links used in an answer. Research can then become proposed changes to the contract, subject to reviewer acceptance. Mass Review separately lets users inspect the contract language underlying a table result. Published totals for the research network differ between the FAQ, guide, and trust center. No reproducible accuracy test, source freshness method, or systematic citation treatment results accompany those totals.
Lexible uses lawyer labeled concepts to identify contract provisions and assess them against a playbook. Its accuracy explanation reports 97% F1, 96% precision and 97% recall, with testing three times weekly against 750,000 verified data points. Legal specialists verify training examples, and sampling covers different contract types and domains. These figures concern classification and do not measure every generated redline or answer. The underlying dataset, current model version and detailed failure results are not available for independent reproduction. The terms acknowledge that training can reduce accuracy, especially with incorrect labels, and that mistakes remain possible.
Autonomy and Oversight Model
What the system decides on its own, what a lawyer must approve, and whether the vendor documents where the review point sits. A tool that drafts under review and a tool that files without one are different products and different risks.
The enterprise workflow divides authority between legal and approved business reviewers. Legal owns playbooks, permissions, acceptable positions, and escalation rules; procurement or contract managers perform a bounded first review on agreed contract types. Issues outside the approved route return to legal or the appropriate specialist with the clause and business context. The product proposes tracked edits and comments in Word, which authorized reviewers can edit, accept, or reject. DraftPilot expressly describes this arrangement as something other than autonomous contracting. Its control boundary is organizational approval and exception routing, rather than a published numeric model confidence threshold.
The platform reads an agreement, identifies issues against a configured playbook and presents suggested redlines in Word. Users can share issues and changes with colleagues, while Lexible Assistant supports questions and remediation. The customer sets its risk policies and remains responsible for decisions based on the resulting analysis, including automated decisions. Terms expressly warn that output is not legal advice and may contain mistakes. These features provide a review surface and organizational standards. The system does not specify when it can approve an agreement without review or how it must escalate and correct an incorrect conclusion.
Operational and Outcome Evidence
Named, dated evidence that the product works in production at real firms or legal departments. Case studies with figures and identified customers count. Unattributed testimonials and launch announcements do not.
Axiom's 2025 white paper describes an eight week pilot in which 18 volunteer attorneys used DraftPilot on client work after an evaluation of eight suppliers. Evidence came from surveys, product analytics, participant calls, and feedback sessions. Forty four percent of participants reported time savings in the 40 to 60 percent band, while 16 of 18 reported moderate or significant improvement in work quality. The vendor's case study also separates tasks such as drafting, issue spotting, and summaries. These are reported observations from motivated volunteers, not a randomized benchmark. Raising Cane's provides another named deployment involving real estate positions and lease review.
A named customer gives figures, without a date or method. Joe Stephenson, partner and head of technology at Shoosmiths, is quoted on the demo page saying the platform reviewed complex supply agreements in under three minutes at above ninety percent accuracy, where lawyers took four hours at 86 percent. A second, unnamed customer calls ThoughtRiver a clear winner for reviewing its appointments. No date, sample size or method accompanies the Shoosmiths figures.
Privilege and Confidentiality Posture
How client confidences are handled: attorney client privilege and work product treatment, segregation of one client matter from another, whether client data trains any model, and what the vendor commits to in writing rather than in marketing.
DraftPilot states that customer material does not train its models or public models, and its security guide says original contracts are not stored after analysis. Playbooks remain in the customer's workspace, with permissions controlling their use and editing. OpenAI inputs and outputs receive the stated zero retention arrangement. The broader trust center also names Anthropic and lists 30 days for generated redlines and comments, which narrows the general no retention headline. No accessible commitment directly addresses attorney client privilege or work product. The linked customer terms require sign in, and the trust center requests access before releasing the service agreement or DPA.
The agreement treats customer contract data as confidential and restricts disclosure by staff and subcontractors, with confidentiality continuing after termination. Customer accounts have separate encryption keys and segregated contract data. Anonymization removes sensitive information before model processing. The same agreement grants continuing use of training data and anonymized contract text, and the DPA includes machine learning training among processing purposes. This is permission to reuse specified material, rather than a promise never to train. Internal matter walls, direct privilege handling and a complete account of every underlying language model provider are not specified.
UPL and Professional Responsibility Posture
Whether the vendor is clear that it supplies a tool rather than legal advice, who its audience is, and how it addresses unauthorized practice of law, competence and supervision duties, and jurisdiction limits. ABA Formal Opinion 512 is the reference point. Where the advice line is not the duty a product raises, the axis is read through the nearest professional duty it does raise: judicial conduct rules and the reviewing duty for products sold only to courts, and the duty to bill for time actually spent for products that draft time entries.
The enterprise operating model keeps legal judgment with lawyers and approved specialists. Procurement and contract managers may conduct an initial review only within legal's chosen contract types, positions, and escalation rules. Suggested language remains subject to acceptance, and exceptional issues return to the legal team. This division keeps assistance separate from professional judgment and reserves consequential advice for legal. The research guide also asks the user to state a jurisdiction and intended use. Public pages do not define professional eligibility by jurisdiction or connect those controls to specific competence and supervision duties.
The customer terms expressly say that output is not legal advice and must not be relied on as advice. They also warn that predictive technology makes mistakes and that incorrect training can worsen accuracy. Customers remain responsible for results, conclusions and automated or manual decisions. The license allows both internal business work and integrated managed services for legal clients, so law firms and other business users share that responsibility. Product review surfaces support human checking, but jurisdiction specific practice limits and an explicit treatment of lawyers' competence and supervision duties are not set out.
AI Governance and Bias Disclosure
Published governance over model behavior: who owns it inside the vendor, what is tested before release, and what is disclosed about disparate output across matter types, parties, or populations.
DraftPilot's trust center publishes principles covering privacy in product design, human empowerment, and transparency through security certification. It also describes output protection, operational guardrails, and grounding of AI Chat in a verified source network. Those statements give a direction for responsible use but do not identify an accountable model owner, a release evaluation procedure, or results across languages and contract types. An AI Governance Policy is listed among resources that require access approval, so its contents cannot establish additional public controls. Customer playbooks and escalation rules govern a customer's review process; they do not disclose the vendor's own model testing.
ThoughtRiver describes lawyer verification of training examples, annotation that accounts for contract context and sampling across contract types and legal domains. Its evaluation program runs three times each week against 750,000 verified data points and reports separate precision, recall and F1 measures. Customers can set playbooks for their own risks and review proposed changes before further use. These are concrete training and evaluation practices. The description does not identify an accountable AI governance owner, a mandatory release approval threshold or findings about uneven output across jurisdictions or populations. Its regulatory position is separate from those operating tests.
AI Safety and Data Stewardship
Retention, deletion, access control, and what happens to prompts and documents after they are processed. Whether the vendor states its subprocessors and its incident practice, or leaves the buyer to assume.
Public resources distinguish contract analysis from stored customer configuration. The security guide says playbooks normally reside on AWS in Ireland and describes immediate deletion of OpenAI API inputs and outputs. The AI posture page separately lists 30 days for generated redlines and comments. Administrators use SSO, SCIM, workspaces, and permissions to control access. Twelve subprocessors are named with service roles, including hosting, model, email, analytics, and business operations providers. Encryption is specified for transit and storage. However, a complete deletion process for retained playbooks, a binding incident notification period, and the DPA terms are not available in the readable public material.
The DPA names infrastructure, authentication, document conversion, email and support providers, and requires equivalent safeguards for subprocessors. Personal data breaches must be reported without undue delay, with cooperation in mitigation. Customer data is encrypted in transit and at rest, separated by account and protected by a dedicated encryption key. Authentication supports single sign on and multifactor controls. At termination, personal data must be deleted or returned, with backup and legal exceptions. A defined in use retention period for prompts, a backup deletion deadline and an explicit list of the language models processing contract text are not specified.
AI Liability and Recourse
What the vendor stands behind contractually when its output is wrong. Indemnities, caps, carve outs, insurance, and whether any of it is published or only reachable through a negotiated agreement.
DraftPilot links to customer terms on SharePoint, but the linked document leads to a Microsoft sign in page. Its public trust center lists a Master Services Agreement and DPA behind an access request. As a result, no readable contract establishes liability caps, indemnity scope, an AI output warranty, or remedies for an incorrect clause or research answer. This is an access limitation, not evidence that the negotiated agreement lacks those provisions. The published review workflow allocates final decisions to authorized people, but that operational allocation does not reveal what financial responsibility DraftPilot accepts when its service makes an error.
ThoughtRiver indemnifies customers against claims that permitted use of the platform infringes third party copyright, subject to control of the defense and exclusions for unapproved combinations. It can replace or modify the platform to address infringement. General liability is limited to the order term's fees, or £1,000 during a trial, with the copyright indemnity excluded from that cap. Death or injury caused by negligence, fraud and other legally unavoidable liabilities also remain outside the limitation. Output errors carry no warranty or accepted liability, so the software indemnity does not amount to a guarantee of correct legal analysis.
Practice Systems Integration Depth
How deeply the product reaches into the systems legal work already lives in: document management such as iManage and NetDocuments, Word and Outlook, contract lifecycle management, matter management, e-billing, and court filing systems.
The Microsoft Word connection has a described installation and editing path. Users install the add in through AppSource or central IT deployment, open it from the Word ribbon, and work with the agreement already being negotiated. Proposed language becomes native tracked changes, while explanatory comments accompany the edits. Reviewers can accept, reject, or alter individual suggestions and handle later rounds by focusing on counterparty changes. AI Chat can insert drafting or tables into the same file. Workspaces and identity controls manage product access. No comparable public sync specification describes a connection to a legal document management repository.
Agreements enter through email, file upload or a Microsoft Word add in, with issues and redlines available for team collaboration. Microsoft 365 supports work in Word, Outlook and Teams, while Power BI receives contract data for analysis. HighQ and iManage are named connections for legal projects and document workflows. The platform also exposes the same public APIs used by its web application and follows the OpenAPI specification. These connections have identifiable purposes, but per connector field mappings, authentication setup, permission inheritance and error recovery steps are not described in the integration overview.
Deployment Model and Data Residency
Where the software runs and where the data sits. Multi tenant cloud, single tenant, private deployment, on premises, and whether region of residence is a published option or an enterprise conversation.
DraftPilot is delivered through a Word add in and a browser interface, with hosted services handling analysis and team configuration. Its security guide places default playbook storage on AWS in Ireland and offers United States only hosting on Enterprise. The subprocessor list separately identifies Heroku in Ireland and OpenAI and Anthropic in the United States. Those provider locations do not establish where every model request is processed. The guide's OpenAI zero retention statement addresses persistence, not processing geography. Public materials therefore identify hosting choices and part of the provider chain without a complete regional account for all AI tasks.
Region and processing location are stated, and tenancy is described as segregation. The security page says data resides in Azure data centers managed and secured by ThoughtRiver in a single specified region, and the subprocessor table places Azure hosting in the United Kingdom. Each customer's data is held separately under its own key. Whether a customer may choose a region other than the UK, and whether infrastructure is shared, are not stated, and where the language models run inference is not addressed.
Security Certifications and Trust Center
Independent attestation a buyer can pull without a sales call: SOC 2, ISO 27001, penetration test summaries, a trust center with current reports and named scope rather than a badge image.
The public trust center names SOC 2 Type II and ISO 27001, with an ISO 27001:2022 certificate and a SOC 2 report listed as resources. Both require access approval, so their audit periods, organizational scope, and qualifications are not visible in the accessible pages. GDPR and UK GDPR are presented as compliance frameworks rather than independent security certifications. Additional resources include a network diagram and CAIQ Lite questionnaire, also behind requests. Product pages specify AES 256 encryption at rest and TLS 1.2 in transit. These published controls support the security description but do not replace examination of the underlying reports.
ISO 27001 certification is stated and identified by number, without a route to the report. The security page gives certificate number 21188-ISMS-001 alongside encryption at rest and in transit. No certifying body, coverage period or route to the certificate or any SOC report is published, and no trust center is published.
Model Supply Chain Disclosure
Which models sit underneath, whose they are, where they run, and whether the vendor commits to telling customers when that changes. A legal buyer inherits every dependency it cannot see.
The main FAQ identifies OpenAI as the provider behind the contract workflow. The trust center supplies a broader inventory: GPT 4 Turbo with an April 2024 version identifier, and Anthropic Claude 3.5 Sonnet with an October 2024 identifier, both accessed through APIs. The subprocessor list confirms both companies and associates them with AI services. The security guide explains OpenAI's zero retention arrangement, but does not extend that provider specific statement expressly to Anthropic. Public material does not map every feature to its active model or commit to customer notification when a model changes. The inventory therefore discloses providers without a complete change framework.
Lexible combines a proprietary legal model with general purpose language models for contract interpretation and generated responses. ThoughtRiver describes supervised learning from expert labeled concepts, legal validation of examples and repeated evaluation. An anonymization layer protects sensitive text before analysis, and a private model option is offered. The hosting environment is Microsoft Azure. External foundation model providers, versions, routing decisions and processing locations for each model are not named. The subprocessor register covers infrastructure and support functions but does not resolve the generative model chain or commit to notice of each model substitution.
Commercial Transparency
Whether a buyer can learn what this costs without entering a sales process: published rates, the unit being charged, what sits behind an enterprise tier, and what implementation adds.
DraftPilot Pro starts at £7,200 per year for a minimum of three users, equivalent to £2,400 per user annually. A separate Customer Success Advisory Fee applies without a published amount. Everyday review and chat are unlimited under the package, while Mass Review includes an allowance and optional additional capacity. Pro includes playbooks, workspaces, identity controls, onboarding, and optimization support. Enterprise uses a negotiated organization package with tailored deployment and ongoing support. The posted subscription amount is therefore a real entry point, but it is not the complete first year cost because advisory charges and additional portfolio analysis remain unspecified.
Professional starts at £15,000 a year in the United Kingdom for teams reviewing fewer than 50 contracts a month; Enterprise starts at £30,000 for higher volume. Outside the United Kingdom, the page displays starting rates of $20,000 and $35,000 respectively. Both list unlimited users, a private database instance, Word access, Lexible Assistant, single sign on and dedicated success and professional services. A trial lasts 28 days. The agreement bills order fees in advance and permits separate professional services charges. The published starting amounts therefore do not settle every implementation charge, volume adjustment or negotiated order term.
Firm and Practice Coverage
Who the product is actually built for. AmLaw, midlaw, small firm and solo, in house departments, government and courts, and which practice areas are supported rather than merely claimed.
DraftPilot primarily addresses corporate legal departments handling repeatable commercial agreements. Its enterprise workflow also supports procurement teams and contract managers operating within legal's approved boundaries. The playbook materials cover agreements such as NDAs, services contracts, statements of work, data processing terms, and reseller arrangements. Axiom's deployment extends the audience to lawyers delivering client services, while Raising Cane's demonstrates lease and real estate work. The FAQ describes review across more than 58 languages, including applying a playbook in a different language from the agreement. That breadth is not accompanied by a jurisdiction by jurisdiction account of supported law or tested performance.
Corporate legal, procurement and business teams use ThoughtRiver to review incoming agreements, and the license also allows managed services delivered to legal clients. Configurable playbooks apply the customer's negotiation standards to recurring contracts. Professional and Enterprise packages distinguish teams below and above 50 reviews per month, with unlimited users in both. Named examples include complex supply agreements and professional appointments. The current site gives different totals for its trained concept library on different pages, so that total does not define a reliable coverage boundary. A complete jurisdiction matrix and unsupported contract types are not stated.
The 12 legal signals, side by side
Recorded rather than graded. These are the questions a practitioner has to answer before a tool touches a client matter, and the answers are taken from public material only.
Client Data in Training
Can material a lawyer puts into this product be used to train a model?
DraftPilot's FAQ says neither it nor OpenAI uses customer data to train models. Its security guide extends that position to contracts and interactions, while the trust center repeats a no training policy. The public customer terms lead to a sign in page and the service agreement requires access approval, so no matching contractual prohibition was verified. The policy is explicit, but the readable evidence does not establish the scope of a binding customer term.
The agreement licenses training data and anonymized contract text for use in the platform by ThoughtRiver and other customers, while excluding customer risk policies from that licensed material. Its DPA expressly lists machine learning training and feature enhancement among processing purposes. These permissions continue beyond the subscription and are separate from customer ownership of uploaded contracts. The public agreement does not provide a general option to exclude those uses.
Prompt and Output Retention
How long does the product keep what a lawyer typed, and can that be set to zero?
The AI posture page distinguishes original contract analysis, marked as having no retention, from generated redlines and comments, which it assigns a 30 day period. The security guide separately says playbooks are stored and OpenAI API inputs and outputs are deleted immediately under its zero retention agreement. These statements concern different data layers. No public setting lets a customer shorten the stored suggestion period or apply zero retention across playbooks and generated work.
The DPA requires deletion or return of personal data at termination, except for backup copies and legally required retention. Trial data is deleted when a customer properly ends the trial. The general agreement separately preserves rights over licensed training data and anonymized contract text. None of these provisions gives prompts or generated answers a defined retention period during an active subscription, and the backup exception has no stated deletion deadline.
Ethical Walls and Matter Segregation
Does retrieval respect the firm’s ethical walls, or can the model read across them?
Workspaces organize individual and team playbooks, and administrators determine who may view, use, or edit them. This gives legal a way to separate ownership of approved positions from business use of those positions. The public instructions do not establish matter based retrieval restrictions, conflict walls, or inheritance of a document management system's permissions. Playbook access controls therefore provide a narrower form of separation than a documented ethical wall around client files.
Contract text and derived data are separated from other customer accounts and encrypted using a key specific to each account. Organization access can use single sign on with multifactor authentication. These are organization level protections. Although iManage and HighQ are named integration options, a mechanism that inherits their per user permissions during retrieval or separates opposing matters within one customer environment is not specified.
Third Party Request and Subpoena Notice
If someone subpoenas the vendor for a firm’s data, does the firm hear about it first?
No readable public commitment establishes whether DraftPilot tells a customer before responding to a subpoena or other demand for customer material. The current terms and privacy links lead to SharePoint sign in, while the trust center's service agreement and DPA require access approval. Its security controls and no training policy do not answer this notice question. The finding records the access limit and does not determine what the negotiated agreement might promise.
When law requires ThoughtRiver to process personal data contrary to customer instructions, the DPA requires notice to the customer unless law prohibits it. That protection covers compelled processing of personal information in uploaded material. The agreement also permits legally required disclosure of confidential information, without a broader notice procedure for nonpersonal material. A public report of government demands and the response to them is not specified.
Primary Law Corpus Provenance
Where does the law in this product come from, and does the vendor have the right to use it?
The research guide describes a curated network containing legislation, secondary sources, and legal commentary across more than 150 jurisdictions; the FAQ gives a larger jurisdiction total. Answers expose the links used, so the reviewer can inspect their immediate basis. A public inventory of named collections, licensing permissions, and update schedules is not supplied. The product therefore describes geographic reach and source categories more clearly than the rights and maintenance arrangements behind the research network.
The engine applies a lawyer developed concept library to customer agreements and risk policies. ThoughtRiver describes millions of labeled concepts and legal verification of training examples, while the agreement defines training data and anonymized contract text available for broader platform use. These identify the main categories behind contract interpretation. The underlying external legal collections, their licenses and the refresh schedule for any legal authority are not specified.
Good Law Verification
Does the product tell you when the authority it just cited has been overruled?
DraftPilot gives users a source list for research answers and asks them to identify the relevant jurisdiction. Those links allow inspection of the cited material, but the public research guide does not describe subsequent case history, treatment flags, or a licensed citator. A real source can still be superseded or limited by later authority. No product mechanism for resolving that separate currency question was identified in the research, FAQ, or trust material.
ThoughtRiver's review concentrates on contract provisions, risk policies, proposed edits and obligations in customer agreements. A reviewer can work through issues in that agreement and use the resulting information for negotiation or reporting. The stated workflow does not include a licensed citator or a method for checking the subsequent treatment of a court decision. Classification accuracy and contract risk ratings do not supply that authority status check.
Refusal and Uncertainty Behavior
What does the product do when the answer is not in the corpus?
The trust center describes grounding and output safeguards, but it does not specify an abstention response when the curated network cannot support an answer. The Issues Report assigns risk to contract departures; that risk describes the agreement against a playbook, rather than model confidence or missing support. Reviewers can reject suggestions and inspect sources, yet those human controls do not show that the system itself refuses an unsupported legal assertion.
Lexible flags contract risks, produces proposed changes and lets a reviewer inspect issues against a playbook. Its precision, recall and F1 figures describe aggregate classification performance rather than a confidence indicator attached to an individual answer. The terms warn that predictive output can be wrong. A stated no answer response, an abstention threshold or a route triggered specifically by insufficient support for a conclusion is not described.
Fabricated Citation Record
Does a public court record exist addressing fabricated or hallucinated legal citations in output from this product?
The AI Hallucination Cases database maintained by Damien Charlotin contains no entry naming DraftPilot or Draft Pilot. The database concerns judicial decisions involving hallucinated AI material and records implicated tools when identified. DraftPilot does provide legal research as well as contract drafting, so source accuracy remains relevant even though its principal workflow concerns negotiation. The absence of a named case does not test its research answers, proposed clauses, or handling of later legal authority.
The AI Hallucination Cases database has no tool entry naming ThoughtRiver or Lexible as of October 15, 2026. The product's described work involves contract provisions, risk review, redlining and questions about agreements rather than preparing a litigation brief. That workflow context is separate from the court record: the absence of a named entry does not measure classification errors or establish that a generated answer is correct.
Bar Guidance Alignment
Has the vendor engaged in public with the ethics opinions its buyers are bound by?
DraftPilot gives legal control over approved positions, access rights, and exceptions, and it describes a supervised role for business reviewers. Those operating controls address practical oversight, but the published enterprise and research materials do not map them to a named bar opinion or jurisdiction's professional conduct guidance. In particular, no client consent method or fee treatment framework is tied to a specific professional rule. Product supervision should therefore be distinguished from demonstrated guidance alignment.
The contract terms distinguish software output from legal advice and leave responsibility for decisions with the customer. They allow managed services for legal clients, and product controls let reviewers apply organizational playbooks and inspect proposed changes. These are general responsibilities and workflow controls. A named ethics opinion or jurisdiction specific bar rule governing a lawyer's use of Lexible is not identified in that position.
Billing and Fee Posture
Does the vendor address what happens to the bill when the work takes an hour instead of six?
Axiom's published DraftPilot offering attaches a $250 monthly technology fee to qualifying client engagements and explains that its lawyers use the tool to save time on contract work. Its white paper explicitly presents those gains as a client benefit. This identifies a disclosed software charge reaching the client through the legal service arrangement, rather than only an internal corporate efficiency claim. DraftPilot's own subscription pricing is separate, and no universal billing policy for other customers is stated.
The license expressly permits ThoughtRiver as part of managed services for a customer's legal clients. Marketing compares minutes of assisted review with hours of lawyer work, and the named Shoosmiths testimonial concerns complex supply agreements. Those savings can affect a client's bill as well as internal capacity. A record designed to allocate assisted work to billable time, or guidance on fees and client disclosure, is not described.
Outside Counsel Guideline Readiness
Can a firm get this vendor through a client’s AI clause without a bespoke negotiation?
A public register names 12 providers and their roles, including OpenAI, Anthropic, hosting, email, analytics, and business support services. The trust center also exposes policy summaries and lists a service agreement, DPA, security reports, and governance policy for access requests. These resources provide material for vendor diligence. They do not include a complete client notification or consent package explaining how a law firm's proposed use satisfies a particular client's outside counsel requirements.
The public register identifies Microsoft Azure in the United Kingdom, SendGrid in the United States, and Okta, ABBYY Vantage and Zoho Desk in Europe. It gives functions, data categories and transfer safeguards for those providers. The list supports part of a client's data handling review, but does not name the general purpose language model providers or supply a ready made client AI consent and disclosure pack.
Court Disclosure Support
If a judge’s standing order requires an AI disclosure, can the product produce one?
Word tracked changes and comments preserve proposed revisions, while research answers expose their source links and the Issues Report identifies clauses, problems, risk, and possible responses. These are useful components of a review record. Inserted changes appear under the user's Office name, so the file alone does not label which work came from AI. No single export combines the generating model, retrieved authorities, and human verification for a court disclosure.
The Digital Issues List preserves flagged issues, risk levels and resolution status and can be shared or downloaded. That gives a contract reviewer a record of what required attention and how the review progressed. Word changes and collaboration add further context. An export combining the exact model, retrieved sources and human verification for each generated passage is not described, so these records do not by themselves form a complete AI disclosure certificate.
The questions both sides leave open
Derived from the records above rather than written, so it cannot favor either vendor. Take these into both conversations and ask each side the same question.
- Ethical Walls and Matter Segregation
- Good Law Verification
- Refusal and Uncertainty Behavior
- Bar Guidance Alignment
Which one fits
Choose DraftPilot if
- The team needs to refine its approved playbook during an ongoing negotiation.
- Different groups will own and use the legal standard under distinct workspace permissions.
- Cited research and drafting chat should sit beside the Word review process.
Choose ThoughtRiver if
- A prepared review framework with several levels of issue coverage is the desired starting point.
- Custom playbook setup should include direct help from the vendor's implementation team.
- Contracts arrive through email and file upload as well as Word.
In summary
DraftPilot
DraftPilot generates playbooks from agreements, policies, and previous markups, then lets legal refine those positions as negotiation changes. During a live Word review, the playbook can be updated without leaving the editing workflow, a feature highlighted by the AI Legal Index. Workspaces define who may edit or use the resulting standard, and exceptions return to the responsible legal team. Alongside redlining, AI Chat drafts language and answers contract questions, while portfolio analysis links findings back to the agreement text.
ThoughtRiver
ThoughtRiver's Lexible platform identifies contract risks and obligations, applies playbooks, and supports redlining through its Assistant. The AI Legal Index highlights the separate Core, Standard, and Comprehensive review settings, which change the breadth of issues considered. Teams can also supply their own forms, precedents, or negotiating rules for a custom playbook with implementation support. Email, file upload, and Word provide entry routes, and portfolio analysis supports wider examination of contract terms.
Questions buyers ask
Do ThoughtRiver's review levels represent model confidence?
No. Core, Standard, and Comprehensive describe different depths of issue coverage and risk tolerance in the prepared playbooks. They are not a statement of confidence in each generated answer. From the AI Legal Index, based on each vendor's own published materials as of October 16, 2026. No vendor pays for placement.
Can ThoughtRiver use an organization's own negotiating rules?
Yes. Its custom playbook service accepts internal rules, forms, or precedents and translates those positions into the review framework. The company also offers implementation support. From the AI Legal Index, based on each vendor's own published materials as of October 16, 2026. No vendor pays for placement.
How does DraftPilot preserve the legal team's control of a shared playbook?
Workspaces and permissions determine who can view, use, or edit positions. Legal retains ownership of the standard and the decisions needed for exceptions outside the approved review path. From the AI Legal Index, based on each vendor's own published materials as of October 16, 2026. No vendor pays for placement.
Can both products examine a portfolio of agreements?
Yes. DraftPilot Mass Review returns a table with access to supporting contract language. ThoughtRiver applies playbooks and contract analysis across a portfolio to expose terms, risks, and obligations. From the AI Legal Index, based on each vendor's own published materials as of October 16, 2026. No vendor pays for placement.
Neither vendor paid for inclusion, placement or a grade, and neither reviewed this page before it published. Everything on it comes from public material on the dates shown. How the index grades.
ThoughtRiver's depth settings concern the scope of contract review. They should not be interpreted as an accuracy comparison with DraftPilot or as a substitute for deciding which legal positions apply to the transaction.